<b>Parsing in Artificial Minds: A Comparative Study of Relative Clause Attachment and Semantic Bias Sensitivity in Arabic- and English-Trained Language Models</b>

Contrary to expectations, Humain AI demonstrated categorical low-attachment preference identical to English models, failing to reflect human Arabic speakers' high-attachment tendency. However, across all models, sensitivity to semantic plausibility cues varied significantly. Claude-Haiku-4.5 exhibited the strongest overall semantic integration (up to 97% congruency in role bias with A/B prompts), while Humain AI and GPT-5.5 showed notably lower performance.

Prompt formatting substantially influenced responses, with A/B prompts consistently eliciting greater semantic sensitivity. Findings challenge the assumption that Arabic-specialized training alone produces human-like language processing. Despite specialized training data, Humain AI exhibited English-like parsing biases, suggesting that large-scale pretraining may not authentically capture language-specific syntactic preferences.

Results underscore that LLM linguistic fluency masks fundamental differences from human parsing mechanisms and highlight the critical role of prompt design in shaping model behavior.</p><p dir="ltr"><br></p>